Panel Data Research Center, Keio University
PDRC Discussion Paper Series

Return to Sleep
Shinya Kajitani
8 October, 2019
DP2019-001
https://www.pdrc.keio.ac.jp/en/publications/dp/5489/

Panel Data Research Center, Keio University
2-15-45 Mita, Minato-ku, Tokyo 108-8345, Japan
info@pdrc.keio.ac.jp
8 October, 2019

Return to Sleep

Shinya Kajitani
PDRC Keio DP2019-001
8 October, 2019
JEL Classification: I12; J24; J31
Keywords: Labor productivity;Sleep duration;Time use;Wage;Working hours

Abstract
Most people spend a substantial amount of time sleeping. I examine the causal effect of sleep on laborproductivity, utilizing panel datasets for Japanese males. I deal with the potential endogeneity of the decision
about how many hours to sleep by using a fixed effects model with an instrumental variables estimation
technique. My findings show that a one-hour increase in weekly sleeping hours increases the wage rate by 4-6%.
Sleep duration does indeed enhance labor productivity for Japanese males.

Shinya Kajitani
Faculty of Economics, Kyoto Sangyo University
Motoyama, Kamigamo, Kita-ku, Kyoto-city
kajitani@cc.kyoto-su.ac.jp

Acknowledgement: This work was supported by JSPS KAKENHI 15K17080.

Return to Sleep
Shinya Kajitani∗
October, 2019

Abstract
Most people spend a substantial amount of time sleeping. I examine the causal eﬀect of sleepon labor productivity, utilizing panel datasets for Japanese males. I deal with the potential
endogeneity of the decision about how many hours to sleep by using a ﬁxed eﬀects model withan instrumental variables estimation technique. My ﬁndings show that a one-hour increase inweekly sleeping hours increases the wage rate by 4–6%. Sleep duration does indeed enhancelabor productivity for Japanese males.

Keywords: Labor productivity, Sleep duration, Time use, Wage, Working hours.
JEL Classiﬁcation: I12, J24, J31.

∗

Corresponding author. Associate Professor, Faculty of Economics, Kyoto Sangyo University, Motoyama, Kamigamo, Kita-ku, Kyoto-city 603-8555, Japan. Phone number: +81-75-705-1739. E-mailaddress: kajitani@cc.kyoto-su.ac.jp

1

Introduction

In the standard labor supply model, individuals make trade-oﬀs between the consumptionof goods and the consumption of leisure, where leisure is deﬁned as time not spent atwork. However, Juster and Staﬀord (1991) point out that it is essential to shed lighton the productive uses of leisure rather than leisure as consumption. For example, Fahr
(2005) examines the determinants of time spent on educational leisure, which means theeducational character of certain leisure activities. Educational leisure has a productivity
component compared with purely consumptive leisure.
Many past studies in economics suppose hours of sleep as exogenous. This is because
a need for sleep is biologically determined. By contrast, when sleep is classiﬁed as a
type of leisure, sleep could be regarded as productive leisure. A human spends many
hours of the day on sleep, which is a physiological function to recover from cerebral
fatigue. Medical studies indicate that sleep plays an essential role in inﬂuencing laborproductivity. Horne (1988) points out that the organ most aﬀected by sleep loss is the
cerebrum. When the human experiences sleep deprivation, the cerebrum requires sleep
for its repair. Walker (2017) summarizes that sleep restores one’s learning ability andoﬀers motor skill improvement while sleep deprivation causes a decline in concentration.
Hafner et al. (2016) suggest that insuﬃcient sleep is costly for employers because it
reduces workplace productivity. These studies suggest that sleep also has a productivity
component. When labor markets are competitive, the derivation of the labor demandschedule leads to a ﬁrst-order condition for the ﬁrm that equates the wage to the marginal
product of labor. In this case, the wage changes derive from labor productivity changes.
If sleep enhances labor productivity, sleep could have positive eﬀects on wages.
Individuals vary in the amount of time that they allocate to sleep. While some part ofthis variation is biologically determined, another part of this variation is related to choices
inﬂuenced by economic incentive, as Biddle and Hamermesh (1990) point out. In their
theoretical model, while sleep is a function of the individuals’ wage rate, their demand
for sleep allows for the possibility that sleep enhances labor productivity. They point out
that the relationship between wages and sleep could be causal in both directions.
In this study, I examine whether sleep aﬀects productivity for Japanese male workers,
using the Keio Household Panel Survey which is a nationally representative survey panel1

in Japan.1 I resolve the endogeneity between wage and sleep by using the ﬁxed eﬀects
model with an instrumental variables estimation technique. To capture a representative of
individuals’ long-run time use, I focus not on the hours of sleep on the day of the interview
but on the usual sleeping hours. I am interested in the sleeping time of individuals overa period of time: person-months or person-years rather than person-days. Meanwhile,
my productivity measures are average hourly labor income and average hourly wages inthe year of the interview.
My contribution to previous studies is twofold. First, to identify the causal relationship between productivity and sleep, I use a panel estimation with instrumental variables
removing both time-invariant and time-variant unobserved heterogeneities among workers. Locations further east will experience an earlier average sunset than areas further
west in the same time zone. When sunset occurs at an early hour, individuals tend to
fall asleep earlier on condition that there is no artiﬁcial light exposure (Walker 2017).
Circadian rhythms are entrained to solar cues, ensuring that behavioral, genetic, andphysiologic rhythms are timed with daily changes in the environment (Czeisler and Gooley 2007). Because of circadian rhythms, melatonin, which induces sleep, is released aftersunset. While time zone cues help to determine social scheduling such as work schedules
and school start times, the social schedules are not so responsive to the solar cues, as
Hamermesh et al. (2008) discuss. For example, if individuals on the late sunset side of atime zone boundary tend to go to bed at a later time, many of them cannot fully compensate in the morning by waking up later (Giuntella and Mazzonna 2019). Thus, sunset
time can have signiﬁcant eﬀects on sleeping hours. Giuntella et al. (2017) identify theeﬀect of sleep on cognitive skills and depression symptoms exploiting Chinese variationin average annual sunset time between cities as an instrument. Both China and Japan
have a single time zone. The sunset time disparities between areas under the single timezone are informative for identifying the eﬀects of sleep as the instrument. However, since
the sunset time disparities depend entirely on longitude, average annual sunset time istime-invariant within cities. That is, the sunset time is a ﬁxed feature of a location. This1

In Japan, women are more likely to quit their jobs after marriage and childbirth than in otherdeveloped countries. Some women return to work after childbirth, and others do not. Besides, thedistribution of average hours of work is diﬀerent between males and females. Thus, working status isquite diﬀerent between males and females during their careers. The analysis of sleep eﬀects on wage forfemales could be a little more complicated than the analysis for males. For these reasons, the analysis
in this study is restricted to Japanese males.

2identiﬁcation strategy did not allow me to employ a ﬁxed eﬀects model when I consider
the time-variant unobserved heterogeneity among workers.
Instead, I exploit variation inaverage annual temperature and average annual sunshine duration between cities as the instruments. One of the most reliable environmental
timing cues that entrains the circadian rhythms is the daily temperature cycle (Buhr etal. 2010). Cable et al. (2007) outline the evidence that the circadian rhythm of coretemperature is aﬀected by ambient temperature. Rifkin et al. (2018) review numerous
physiologic studies and point out that the thermal environment is a signiﬁcant determinant of sleep quality. Frazis and Stewart (2012) point out that the instruments should
predict long-term time-use, rather than short-term, when the dependent variable is along-term outcome such as productivity or wages. Average annual temperature (averageannual sunshine duration) is a general long-term indicator of the heat (cloudiness) of thelocation. Some studies show that there is no signiﬁcant long-term relationship between
climate and one’s performance or productivity (e.g., Graﬀ Zivin et al. 2018), while there
is a signiﬁcant short-term relationship between climate and one’s performance or productivity (e.g., Graﬀ Zivin and Neidell 2014; Graﬀ Zivin et al. 2018). These indicate thatthe regional diﬀerences in annual weather condition enable us to identify the eﬀects of
the usual sleeping hours as the instruments in the ﬁxed eﬀects model when considering
the time-variant unobserved heterogeneity among workers.
Second, I examine the Japanese case, which is an excellent example for ﬁnding a relationship between hours of sleep and productivity. Japan has, indeed, been categorized
in the group of OECD countries with the shortest sleeping hours. According to OECD
(2009), the Japanese slept the second least in the 18 OECD countries surveyed in 2006,
while the French enjoyed one hour more sleep than the Japanese. I show that sleep is avital wage determinant in Japan and, therefore, an essential part of a Japanese individual’s time-allocation decision. My empirical results should contribute to the accumulating
studies on this topic.
The estimation results considering both the time-invariant and the time-variant unobserved heterogeneities showed that a one-hour increase in weekly sleeping hours forJapanese males increases both hourly labor income and hourly wage by 5–7%. In thecase of regular employees, a one-hour increase in weekly hours of sleeping increases bothhourly labor income and hourly wage by 3–5%. The rest of this paper is organized asfollows. In Section 2, I review previous studies which document the relationship between
3

sleep and wage. Section 3 describes the data, and Section 4 presents the empirical framework. Section 5 presents and discusses the estimation results, and Section 6 concludes.

2

Previous Studies

There are several trends in sleeping hours in developed countries. Juster and Staﬀord(1991) examine the changes in average hours of sleep between the 1960s and 1980s andshow that weekly sleeping hours decrease by 0.8 hours for males and 1 hour for females in
the United States, and by 3.8 hours among men and 2.6 hours among women in Japan.
Meanwhile, Gimenez-Nadal and Sevilla (2012) ﬁnd that average weekly sleeping hours
increase for both men and women in Canada, Finland, France, the Netherlands, and theUnited Kingdom from the 1970s until 2000. Kuroda (2010) points out that the UnitedStates has a slight increasing trend in sleep duration in the ten years between 1991 and2001, although Japan has a decreasing trend in sleep duration after 1991. Basner andDinges (2018) also show that average sleep duration in the United States increases after2003.
When individuals get more (less) sleep, they need adjustments that allow for extended(shortened) hours of sleep while satisfying the time budget constraint. One of the modiﬁcations is that they increase (decrease) their sleep duration by allocating less (more) tothe market sector. Some studies ﬁnd that market work has a substitution relationshipwith sleep. Brochu et al. (2012), using Canadian time use data, and Antillón et al.
(2014), using American time use data, ﬁnd that a higher unemployment rate, that is,deterioration in the economic condition, is associated with longer sleep duration. Aguiar
et al. (2013), who use data from the American Time Use Survey between 2003 and 2010,
examine how individuals allocate their time during a recession and show that hours of
sleep increase by 1% when market work hours fall by 10%. Gimenez-Nadal and Molina(2015) examine the relationship between health status and time allocation taking into
account that the more time individuals devote to another applies on time. Using time-use
data in six European countries, they show that there are signiﬁcant correlations between
hours of paid-work and hours of sleep.
Biddle and Hamermesh (1990) is the ﬁrst study to demonstrate that some variationin time spent sleeping may result from a choice in response to changing economic incentives. They emphasize that time spent sleeping could be aﬀected by wages. Assuming4

that sleep is an entirely time-intensive commodity whose consumption provides utility inthe same way as other products do, higher wages make individuals’ sleep less because theopportunity cost of sleeping is higher. However, sleep can add to the individuals’ productivity: That is, sleep has a productivity component. The idea that sleep and productivity
are related is supported by evidence from some sleep-related studies. Recent studies also
demonstrate that insuﬃcient sleep is costly for employers by reducing workplace productivity. For example, Hafner et al. (2016) using large-scale employer-employee datasets in
the United Kingdom suggest that workers who sleep less than six hours per day reporton average about a 2.4 percentage point higher productivity loss due to absenteeism orpresenteeism than workers sleeping between seven to nine hours per day. Biddle andHamermesh (1990) layout a simple model of time spent sleeping as a choice variable
where demand for sleep allows for the possibility that sleep enhances labor productivity,
it is also assumed to generate utility, and ﬁnd that higher wages are associated with lesssleep. Asgeirsdottir and Zoega (2011) extend Biddle and Hamermesh’s contribution bymodeling the decision to sleep as an investment decision in the level of daytime alertness(e.g., that an individual enjoys during the day). They demonstrate that higher wages
will make the worker sleep less even when assuming sleep has a positive eﬀect on wages.
These indicate that the relationship between wages and sleep could be causal in both
directions.
Considering the endogeneity between wages and sleep, some previous studies examine
the eﬀect of sleep on wages. Gibson and Shrader (2018) investigate the short-run andlong-run eﬀects of sleep on wages (earnings) in the United States by exploiting variationin sunset time within US time zones. When examining the long-run eﬀect of sleep on
wages (earnings), Gibson and Shrader (2018) use average annual sunset time as an instrument. Their identiﬁcation strategy is based on the fact that locations farther east
experience earlier average sunset than areas farther west in the same time zone. Circadian rhythms cause melatonin to be released after sunset, and melatonin induces sleep.
As previous studies in neuroscience point out, the earlier sunset occurs, the earlier individuals tend to fall asleep on condition that there is no artiﬁcial light exposure. By
contrast, as Hamermesh et al. (2008) suggest, social scheduling such as work schedulingis not so responsive to solar cues. These indicate that the sunset time disparities between
areas under the same time zone are informative for identifying the eﬀects of sleep as the
instrument. Gibson and Shrader (2018) demonstrate that a 1-hour increase in average
5weekly sleep increases wages (earnings) by 8% (5%) in the long-run.
However, Gibson and Shrader (2018) do not examine the causal relationship between
wages and sleep considering time-invariant unobserved heterogeneities among workers.
The ﬁxed eﬀects instrumental variables (FEIV) procedure is useful to identify the causal
relationship between wages and sleep removing both time-invariant and time-variant
unobserved heterogeneities among workers. When using the FEIV procedure, exclusion
instruments are required to be time-variant. However, because sunset time depends
entirely on latitude (longitude), average annual sunset time is time-invariant within cities.

3

Empirical Framework

My identiﬁcation strategy exploited the variation in average annual temperature andaverage annual sunshine duration between cities, while controlling for time-invariant individual characteristics. I consider the following model:ln (Wit ) = α1 Sleepit + Xit α2 + uit ,
uit = µi + σiti = 1, . . . , N, t = 1, . . . , Ti

(1)
(2)

where Wit denotes average hourly labor income (average hourly wage) for individual iat the time of survey t, Sleepit is the usual nighttime sleeping hours for individual i at
the time of survey t. Xit denotes a vector of time-variant control variables: Age andAge squared, which control for age-related eﬀects; Tenure and Tenure squared, which
control for tenure-related eﬀects; a 0–1 dummy variable Union, which takes the value
one if there is a union at the respondent’s workplace and 0 otherwise. Some studies
demonstrate that unions in Japan contribute to an increase in the average wage (e.g.,Hara and Kawaguchi 2008). To take family structure into account, I also include a 0–
1 dummy variable Married, which takes the value one if the respondent has a spouseand zero otherwise, and Number of child under aged 6, which controls for the number of
dependent children who are aged under six. N is the number of individuals and Ti is thenumber of observations available for individual i indicating that I have an unbalanced
panel. As equation (2) indicates, uit is an error term which consists of a time invariant
individual ﬁxed eﬀect µi and an idiosyncratic error σit . The coeﬃcient α1 in equation (1)captures the eﬀect of sleep on wages.
6

The possibility of the endogeneity of the respondents’ hours of sleep Sleepit in equation(1) is an obstacle to estimating the causal eﬀect of sleep on wages. Given the discussion in
Section 2, there would be a reverse causality between sleep and wages; higher wages raise
the opportunity cost of sleeping time; sleep may be correlated with unobservable worker
characteristics that also inﬂuence wages. To avoid this endogeneity problem, I employ theﬁxed eﬀects model with an instrumental variables estimation technique. For equation (1),the FEIV procedure is to ﬁnd time-variant variables that are related to an individual’s
hours of sleep but are unrelated to his wages. I use an instrumental variables strategy that
exploits diﬀerences in average annual temperature and average annual sunshine duration
across Japanese cities as sources of exogenous variation in hours of sleep. I assume thefollowing equation to explain an individual’s sleeping hours:

Sleepit = β1 T empct + β2 Sunct + Xit β3 + eit ,
eit = µi + ηit

(3)
(4)

where T empct is the average annual temperature for city c where individual i lives at thetime of survey t, and Sunct is the average annual sunshine duration for city c where individual i lives at the time of survey t. eit is an error term which consists of a time-invariant
individual ﬁxed eﬀect µi and an idiosyncratic error ηit . Frazis and Stewart (2012) pointout that the instruments should predict long-term time-use when the dependent variable
is a long-term outcome such as productivity or wages. To identify the causal relationship between sleep and wages removing both time-variant unobserved heterogeneities, I
exploit regional variation in annual weather conditions; average annual temperature, andaverage annual sunshine duration as the instruments. Both average annual temperature
and average annual sunshine duration are general long-term climate indicators of the location. Graﬀ Zivin et al. (2018) reveal that there is no signiﬁcant long-term relationshipbetween climate and one’s performance or productivity.
Prolonged periods of high temperatures lead to persistently high nighttime temperatures in the United States (Melillo et al. 2014). Buhr et al. (2010) point out that one of
the most reliable environmental timing cues that entrain circadian rhythms is the daily
temperature cycle. Cable et al. (2007) outline the evidence that the circadian rhythmof core temperature is aﬀected by ambient temperature. Some physiologic studies reportthat human sleep is highly regulated by temperature. Obradovich et al. (2017) examine
7

the association between self-reported sleep and objectively measured temperature geolocated to the city level and show that a one-degree Celsius deviation in monthly nighttime
temperature is associated with an increase of three nights of insuﬃcient sleep per 100
individuals. Meanwhile, there is a positive relationship between sunshine duration andtemperature. van den Besselaar et al. (2015) conﬁrm a strong relationship between sunshine duration and temperature trends over Europe since the second half of the twentiethcentury.
Figure 1 illustrates the annual variation in the long-term climate indicators among
cities, at the nearest observatory from the KHPS respondent’s residential area. As shownin Panel A, the average temperature decreases at higher latitudes and longitudes, while
there is less annual variation in average temperature within cities. By contrast, in PanelB, there is a large annual variation in average sunshine duration within cities, while
average sunshine duration is relatively less associated with latitude and longitude.
[Figure 1 around here]

4

Data: Overview of the Keio Household Panel Survey

My data were drawn from Wave 1, conducted in 2004, to Wave 14, conducted in 2017, of
the Keio Household Panel Survey (KHPS), which is one of the Japan Household Panel
Survey (JHPS/KHPS) datasets. The ﬁrst wave of the KHPS was conducted in the periodof January–March 2004 with a sample size of approximately 4,000 households nationwide.
To compensate for sample attrition, approximately 1,400 and 1,000 new survey households were recruited in 2007 and 2012, respectively. The KHPS aims to investigate various
aspects of household behavior, including time allocation and labor supply behavior. It
contains information on households, families, and individuals. Because, for married couples, the KHPS asks identical questions of both the respondent and his/her spouse, theﬁrst wave contains detailed data on approximately 7,000 individuals.2
The KHPS datasets, excluding 2004 and 2007, contain information on average sleep
duration. Respondents are asked to report how long they sleep each day, on average.
2

Detailed information on the sample design of KHPS is available in Kimura (2005) and from URL:
https://www.pdrc.keio.ac.jp/en/paneldata/datasets/jhpskhps/

8

Using this question, I calculate a measure of weekly sleep duration in hours (hours of
sleep they usually take per day ×7).3 By contrast, the KHPS asks the respondents who
performed paid work or took leave from work in the month prior to the survey about theamount of pre-tax income from their main job in the previous year. The questionnairealso asks about the average weekly hours of paid work. Using this information, I calculate
the hourly labor income: (the amount of pre-tax income from their main job ÷ [average
weekly hours of paid work × 4 (weeks) times 12 (months)]).
The deﬁnitions of all the variables used in the analysis in this study are summarizedin Appendix Table A1. Table 1 shows descriptive statistics on all the variables used in
the analysis. The sample is restricted to male individuals who meet the following threecriterion: (1) the respondent performed paid work or took leave from work at the timeof the survey; (2) information on all the relevant variables is available for the respondentand (3) the respondent has more than two observations (in other words, the respondent is
not included in singleton groups). Appendix Table A2 indicates the eﬀect of this criterionon the sample size available. It is noted that when the primary respondent is married
and is female, the questionnaire also contains virtually identical questions to be answeredby her spouse. Therefore, I include her spouse (male) in the sample used in this analysis.
[Table 1 around here]

5

Estimation Results

Table 2 shows the estimation results using equation (1) by an ordinary least squares(OLS) estimator that ignores both the time-invariant and the time-variant unobserved
heterogeneities among individuals (Column (1)), a Fixed Eﬀect (FE) estimator that ignores the time-variant unobserved heterogeneities among individuals (Column (2)) and a
FEIV estimator that takes account of both the time-invariant and the time-variant unobserved heterogeneities among individuals (Column (3)). Examining the results in Column(1a), I ﬁnd that, after controlling for the control variables, the estimated coeﬃcient of
Weekly sleep is insigniﬁcant. By contrast, in Column (2a), I ﬁnd that, after additionally3

From 2014, the KHPS asks the respondents about their daily average sleep hours both on weekdays
and weekend days, on average. I calculate the measure of weekly sleep hours for the respondents asfollows: (hours of sleep they usually take per weekday ×5) + (hours of sleep they usually take per weekend
day ×2)9

controlling for the time-invariant unobserved heterogeneities, the estimated coeﬃcientof Weekly sleep is positive and signiﬁcant. Because the results reject the null hypothesis that all the time invariant individual eﬀects are jointly zero as shown in Column(2a), controlling for the time-invariant unobserved heterogeneities is required. In Column (3a) where the time-variant unobserved heterogeneities are additionally controlled,
the estimated coeﬃcient of Weekly sleep is also signiﬁcantly positive.
[Table 2 around here]
Examining the endogeneity test which tests the joint signiﬁcance of a residual added
to the model estimated by FE (see Wooldridge 2010, p. 354), as shown in Column(3a), the results reject the null hypothesis that Weekly sleep is exogenously determined.
The Kleibergen-Paap rk Wald F statistic indicates that there is no problem of “weak”
instruments, and an over-identiﬁcation test of all instruments indicates that the FEIV
estimator is to be preferred.4
It is noted that most ﬁrms set a mandatory retirement age in Japan. In the 2013
legislation, the Japanese government imposed job security for employees until the ageof 65. However, most ﬁrms are not willing to extend the mandatory retirement age
but are willing to re-contract at a lower wage with their “ﬁrst-time retirees,” who haveworked for them until mandatory retirement age.5 Most Japanese ﬁrms lower the wagesof elderly workers once they have reached their mandatory retirement age. This is very
diﬀerent from the case in many other countries, including the United States, where law
and custom bar employers from cutting older workers’ pay and beneﬁts when they reacha certain age. Taking a large wage reduction for these elderly employees into account, I
show the estimation results excluding the individuals aged 60 and over from the sample
in Columns (1b), (2b) and (3b). While the coeﬃcient of Weekly sleep is insigniﬁcantestimated by OLS (Column (1b)), both the coeﬃcients of Weekly sleep estimated by FE(Column (2b)) and FEIV (Column (3b)) are signiﬁcantly positive.
It is noteworthy that the magnitude of the coeﬃcient of Weekly sleep in Columns (3a)and (3b) is greater than that in Columns (2a) and (2b). This suggests that there is a
4

Appendix Table A3 presents the results of the estimating equation (3). The exclusion restriction
variables are individually signiﬁcant, and the null hypothesis that all the coeﬃcients on the exclusion
restriction variables are jointly zero is also rejected.
5
According to the “2017 General Survey on Working Conditions (Shūrō Jōken Sōgō Chōsa)” conducted by the Ministry of Health, Labor and Welfare, 93.4% of ﬁrms with more than 30 employees havea uniform retirement age and 79.3% of them set 60 as the retirement age.

10

reverse causality between sleep and wages such that a higher opportunity cost of sleeping
time reduces time spent sleeping, as Biddle and Hamermesh (1990) and Asgeirsdottir
and Zoega (2011) point out. An increase in the wage rate generates both income andsubstitution eﬀects. The eﬀect of the wage rate on sleep duration depends on whether
the substitution eﬀect dominates the income eﬀect or vice versa. My estimation results
demonstrate that the substitution eﬀect may dominate the income eﬀect.
Interestingly, when focusing only on regular employees, the diﬀerence of the coeﬃcients of Weekly sleep between Columns (3c) and (2c) is 0.033 (0.037 − 0.004). This value
is smaller than the diﬀerence between those in Columns (3a) and (2a) (0.055 − 0.003 =0.052). As for the regular employees aged under 60, the diﬀerence value between thosein Columns (3d) and (2d) (0.044 − 0.004 = 0.04) is smaller than the diﬀerence value
between those in Columns (3b) and (2b) (0.067 − 0.003 = 0.064). These results indicatethat the eﬀect of the wage rate on hours of sleeping would be small for regular employees.
Raising the opportunity cost of sleeping time could induce substitution away from sleep
and towards market work: that is, hours of market work may be longer. The change
in labor supply can be decomposed into two labor supply behaviors: extensive margin,indicating a worker’s entry and exit from the labor market; and intensive margin, indicating changes in working hours in response to a wage change. Kuroda and Yamamoto(2008) examine labor supply elasticity on intensive margins in Japan. Considering theendogeneity of wages and hours of work, they demonstrate that the labor supply elasticity on intensive margins is low for both males and females. One of the reasons for the
low elasticity of male labor supply seems to reﬂect the fact that men, as regular workers,
are under strong constraints of the given labor market contracts and customs. As for the
regular employees, raising the opportunity cost of sleeping time may have relatively lessof an eﬀect on the substitution away from sleep and hours of market work.
Using another productivity index: the hourly wage rate, I can see that the estimatedcoeﬃcients of Weekly sleep are signiﬁcantly positive (Columns (a)–(d) in Table 3).6 These
results suggest that sleep duration enhances labor productivity for Japanese males.
[Table 3 around here]
6

While the primary respondent in the KHPS was selected by stratiﬁed two-stage random sampling,
the respondent’s spouse was not selected by random sampling. Even when I exclude the male sample
who are spouses of the primary respondent from this analysis, the estimation results are similar to thoseshown in Tables 2 and 3. These results are available upon request.

11

6

Concluding Remarks

This study examined how sleep aﬀects productivity for Japanese male workers, using the
KHPS which is a nationally representative survey panel in Japan. To resolve the endogeneity between wages and sleep due to both time-invariant and time-variant unobserved
heterogeneities, I use the ﬁxed eﬀects model with an instrumental variables estimation
technique. I focus on usual sleeping hours, which is a representative of individuals’ longrun time use and average hourly labor income (average hourly wage) as productivity
measures for workers.
It is found that a one-hour increase in weekly sleeping hours for Japanese males
increased both hourly labor income and hourly wage by 5–7%. This ﬁnding is consistent
with Gibson and Shrader (2018) that a 1-hour increase in average weekly sleep increases
wages and earnings by 5–8% in the long-run. Besides, as for regular Japanese employees,
a one-hour increase in weekly hours of sleeping increased both hourly labor income andhourly wage by 3–5%. Thus, sleep duration does indeed enhance labor productivity forJapanese males.

12Acknowledgements
I would like to grateful to Miki Kohara, Colin McKenzie, Haruko Noguchi and Isamu
Yamamoto for their extremely constructive and helpful comments. I also would like tothank Junya Hamaaki, Charles Yuji Horioka, Wataru Kureishi, Yoko Niimi, Kei Sakata,
Midori Wakabayashi, and participants at the annual meeting on Japanese Economic Association 2017 Spring Meeting (Ritsumeikan University) and the 15th Annual Conferenceof Japan Health Economics Association (The International University of Health and Welfare) for their helpful comments. This study used data from the Japan Household Panel
Survey(JHPS/KHPS), conducted by the Panel Data Research Center at Keio University.
This study is supported by JSPS KAKENHI Grant Number 15K17080 for a project on“Allocation of Time, Health Disparity and Inequality in Productivity.”

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16

[Appendix Table A1 around here][Appendix Table A2 around here][Appendix Table A3 around here]

17

Figure 1: Annual Variation in the Long-term Climate Indicators among Cities
Panel A: Average Annual Temperature Disparities (degrees C)
25

20
2005

15

2008
10

2011

5

2014
2017

Temperature (degrees C)

Temperature (degrees C)

25

0

20
2005

15

2008
10

2011

5

2014
2017

0
125

130

135
East longitude

140

145

25

30

35
North latitude

40

45

Panel B: Average Annual Sunshine Duration Disparities (hours)
250

200
2005

150

2008
100

2011

50

2014
2017

0

Sunshine duration (hours)

Sunshine duration (hours)

250

200
2005

150

2008
100

2011

50

2014
2017

0
125

130

135
East longitude

140

Source: Author's calculations using data from KHPS.

145

25

30

35
North latitude

40

45

Table 1: Descriptive Statistics
Observations=19,398
Mean Std. Dev.
Min
Weekly sleep
46.14
7.29
12
ln(Hourly labor income)
7.72
0.78
3.37
ln(Hourly wage)
7.72
0.73
3.44
Age squared
2509.081120.76
400
Age
48.78
11.37
20
Tenure squared
424.43516.020
Tenure
16.55
12.28
0
High school
0.44
0.50
0
Junior/technical college
0.07
0.26
0
University
0.35
0.48
0
Graduate school
0.03
0.18
0
Other
0.05
0.21
0
Union
0.37
0.48
0
Married
0.88
0.32
0Number of child under aged 6
0.15
0.44
0
Average annual sunshine hours
162.56
17.29
51.9
Average annual temperature
15.36
2.27
5
Source: Author's calculations using data from KHPS

Max
84
12.22
12.22
7225
85
3364
58
1
1
1
1
1
1
1
4
212
24.1

Table 2: The Effects of Sleep on Hourly Labor IncomeAll

Weekly sleep
Age squared
Age
Tenure squared
TenureHigh school
Junior/technical college
University
Graduate school
Other
Union
MarriedNumber of child under aged 6

Total
(1a)
(2a)
OLS
FE
0.001
0.003**[0.001][0.001]-0.001*** -0.001***[0.000][0.000]0.049*** 0.094***[0.011][0.009]-0.001*** -0.001***[0.000][0.000]0.033*** 0.035***[0.004][0.003]0.052[0.056]0.223***[0.066]
0.294***[0.059]0.575***[0.079]0.034[0.070]0.217*** 0.048**[0.019][0.024]
0.296*** 0.001[0.033][0.047]-0.046*** 0.025*[0.017][0.015]
19,39819,398
2,983
0.18
0.05
69.10*** 51.84***

(3a)
FEIV
0.055***[0.020]-0.001***[0.000]0.089***[0.010]-0.001***[0.000]0.037***[0.004]0.051*[0.026]-0.052[0.058]0.009[0.017]
19,3982,983

Aged under 60
(1b)
(2b)
(3b)
OLS
FE
FEIV
0.002
0.003*** 0.067***[0.001][0.001][0.022]-0.001*** -0.001*** -0.001***[0.000][0.000][0.000]0.063*** 0.103*** 0.089***[0.009][0.012][0.014]-0.000*** -0.000** -0.000***[0.000][0.000][0.000]0.025*** 0.024*** 0.027***[0.003][0.004][0.005]0.054[0.063]0.209***[0.071]0.271***[0.064]0.516***[0.085]0.066[0.076]0.238*** 0.039
0.043[0.020][0.026][0.030]0.268*** 0.006-0.047[0.033][0.050][0.064]-0.030*0.027*
0.005[0.016][0.015][0.019]16,053
16,053
16,053
2,565
2,565
0.19
0.04
68.14*** 39.34*** 30.46***

Regular Employees
Total
(1c)
(2c)
OLS
FE
0.003**
0.004***[0.001][0.001]-0.001*** -0.001***[0.000][0.000]0.064*** 0.102***[0.009][0.013]-0.000** -0.000***[0.000][0.000]0.020*** 0.024***[0.003][0.004]0.151**[0.070]0.302***[0.075]0.386***[0.071]0.610***[0.082]
0.198**[0.084]0.197*** 0.038[0.019][0.024]0.211*** -0.074[0.033][0.046]-0.039** 0.015[0.016][0.015]
13,06813,068
2,128
0.213
0.045
58.09*** 35.15***

(3c)
FEIV
0.037**[0.017]-0.001***[0.000]0.094***[0.014]-0.000***[0.000]0.025***[0.004]0.041[0.025]-0.109**[0.052]0.005[0.017]
13,0682,128

Aged under 60
(1d)
(2d)
(3d)
OLS
FE
FEIV
0.003**
0.004*** 0.044**[0.001][0.001][0.018]-0.001*** -0.001*** -0.001***[0.000][0.000][0.000]0.056*** 0.095*** 0.085***[0.010][0.013][0.014]-0.000*-0.000** -0.000**[0.000][0.000][0.000]0.021*** 0.021*** 0.023***[0.003][0.004][0.005]0.146**[0.074]0.294***[0.079]0.372***[0.075]0.597***[0.088]0.192**[0.086]0.188*** 0.036
0.041[0.019][0.025][0.027]0.202*** -0.065-0.110**[0.034][0.047][0.055]-0.034** 0.013
0.001[0.017][0.016][0.015]
12,19012,19012,190
2,005
2,005
0.220
0.043
58.94*** 33.86*** 30.16***

Observations
Number of individuals
R-squared
F test H0: all the coef. (except the constant)
43.09***
31.95***
are jointly zero
F test H0: all the time invariant individual
6.45***
5.56***
5.36***
5.17***
effects are jointly zero
Endogeneity test (T statistic)-2.73***-3.11***-1.97**-2.25**
H0: Weekly sleep is an exogenous variable
Kleibergen-Paap rk Wald F statistic for weak
19.13
15.94
19.34
17.25
instruments
Over-identification test of all instruments
0.09
0.11
0.00
0.69
(Hansen J statistic)
1) *, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
2) Figures reported in square brackets are robust standard errors adjusted for clustering. The Kleibergen-Paap rk Wald F statistic reported is computed using the "xtivreg2" command in STATA 15.
3) The model in Columns (1a), (1b), (1c) and (1d) additionally includes Constant and Year dummies. Estimates associated with these variables are not reported.

Table3: The Effects of Sleep on Hourly Wage

Weekly sleep
Age squared
Age
Tenure squared
TenureUnion
MarriedNumber of child under aged 6

All
Regular Employees
Aged under
Aged under
Total
Total
60
60
(a)
(b)
(c)
(d)
FEIV
FEIV
FEIV
FEIV
0.059***
0.066***
0.043**
0.049***[0.020][0.022][0.017][0.018]-0.001*** -0.001*** -0.001*** -0.001***[0.000][0.000][0.000][0.000]0.087***
0.089***
0.090***
0.080***[0.010][0.014][0.013][0.014]-0.000*** -0.000-0.000**-0.000[0.000][0.000][0.000][0.000]0.024***
0.016***
0.018***
0.016***[0.003][0.004][0.004][0.004]0.041*
0.050*
0.032
0.039[0.024][0.026][0.024][0.026]-0.097*-0.100*-0.109**-0.110**[0.051][0.055][0.051][0.054]-0.009-0.010-0.007-0.010[0.017][0.018][0.017][0.018]19,39816,05313,06812,190
2,983
2,565
2,128
2,005
30.85***
24.36***
27.12***
24.77***
19.13
15.94
19.34
17.25

Observations
Number of individuals
F test H0: all the coef. are jointly zero
Kleibergen-Paap rk Wald F statistic for weak
instruments
Overidentification test of all instruments (Hansen J
0.41
0.02
0.00
0.75
statistic)
1) *, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
2) Figures reported in square brackets are robust standard errors adjusted for clustering. The KleibergenPaap rk Wald F statistic reported is computed using the "xtivreg2" command in STATA 15.

Appendix Table A1: Definitions of Variables
Variables
Weekly sleepln(Hourly labor income)ln(Hourly wage)

Age
TenureThe respondent's highest level of
educational background(benchmark=Junior high school)
High school

Difinition=hours of sleep the respondent usually takes per day times 7 if before
Wave 10, and =(hours of sleep he usually take per weekday times5)+(hours of sleep he usually take per weekend day times 2) if after
Wave 11.
=the natural logarithm of (the amount of the respondent's yearly pre-tax
income from his main job divided by the total amount of hours of his
paid work a year).
the total amount of hours of his paid work a year=hours of his paid work
per week (including overtime) times 4 weeks times 12 months.
=the natural logarithm of (the amount of the respondent's monthly salary
divided by the total amount of hours of his paid work a month) if hiscompensation type is monthly salary, =the natural logarithm of (theamount of his weekly salary divided by the total amount of hours of his
paid work a week) if his compensation type is weekly salary, =thenatural logarithm of (the amount of his daily wage divided by the total
amount of hours of his paid work a day) if his compensation type is daily
wage, =the natural logarithm of (the amount of his hourly wage) if hiscompensation type is hourly wage, and =the natural logarithm of (theamount of his annual salary divided by the total amount of hours of his
paid work) if his compensation type is annual salary, respectively.
Respondent's age in years at the time of the survey
When the respondent answer the KHPS questionnaire for the first time:=(each year minus year when the respondent started working at his
present company or organization)
When he answer it after second time: =(each year minus year when hestarted working at his present company or organization plus 1) if he wasworking at the same company or organization as 1 year ago, and =0.5 if
he was at a different company or organization from 1 year ago or he was
newly employed during the past year.

=1 if he completed high school, and 0 otherwise.

Junior/technical college=1 if he completed junior or technical college, and 0 otherwise.

University=1 if he completed university, and 0 otherwise.

Graduate school=1 if he completed graduate school, and 0 otherwise.

Other=1 if he completed other school, and 0 otherwise.

Union=1 if there is a union at the respondent's workplace, and 0 otherwise.

Number of child under aged 6

The number of the respondents' children who are aged under 6.

Married=1 if the respondent has a spouse, =0 otherwise.

Average annual temperature=(the summation of monthly average temperature at the nearest
observatory from the respondent's residential area) divided by 12. The
observatory data is provided by Japan Meteorological Agency.
=(the summation of sunshine hours in the month at the nearest
observatory from the respondent's residential area) divided by 12. The
observatory data is provided by Japan Meteorological Agency. Sunshine
hours is defined as the hours during which direct solar irradiance
exceeds a threshold value of 120 watts per square meter (W/m2)

Average annual sunshine duration

Appendix Table A2: Effect of Selection Criterion on Sample Sizes

Wave 1
Wave 2
Wave 3
Wave 4
Wave 5
Wave 6
Wave 7
Wave 8Wave 9Wave 10
Wave 11
Wave 12
Wave 13
Wave 14Total

A: The respondent
B: The spouse of the respondent
Total (A+B)
Eliminating observations
Original
Eliminating observations
Original
Eliminating observations
Eliminating
Eliminating
Original
where the respondent
Number ofwhere the respondent
Number ofwhere the respondent observations where
singleton
Number of
performed paid work or spouse of the performed paid work or spouse of the performed paid work or
the relevantgroups (groups
respondents
took leave from work at respondents took leave from work at respondents took leave from work at information waswith only one(Males)the time of the Wave
(Males)the time of the Wave
(Males)the time of the Wave
not available
observation)
2,000
1,724
1,422
1,225
3,422
2,949
0
0
1,650
1,428
1,215
1,045
2,865
2,473
1,672
1,416
1,436
1,233
1,072
912
2,508
2,145
1,542
1,478
1,944
1,696
1,555
1,324
3,499
3,020
0
0
1,759
1,511
1,421
1,185
3,180
2,696
1,972
1,897
1,636
1,392
1,310
1,075
2,946
2,467
1,832
1,806
1,528
1,272
1,250
1,018
2,778
2,290
1,667
1,659
1,444
1,181
1,188
947
2,632
2,128
1,539
1,534
1,866
1,546
1,485
1,213
3,351
2,759
1,954
1,850
1,734
1,439
1,357
1,086
3,091
2,525
1,735
1,714
1,613
1,316
1,260
1,002
2,873
2,318
1,663
1,656
1,522
1,236
1,166
932
2,688
2,168
1,570
1,564
1,436
1,151
1,102
868
2,538
2,019
1,477
1,470
1,331
1,068
996
793
2,327
1,861
1,364
1,354
22,89919,193
17,79914,62540,69833,81819,987
19,398

Appendix Table A3: Check of the Exclusion Restriction Instrumental Variables (Fixed Effect Model)
All
Regular Employees
Aged under
Aged under
Total
Total
60
60
(a)
(b)
(c)
(d)
Average annual temperature-0.258*** -0.255***-0.287**-0.224**[0.096][0.098][0.125][0.113]
Average annual sunshine hours-0.021*** -0.020***-0.026*** -0.025***[0.004][0.004][0.004][0.005]Observations
19,39816,05313,06812,190
Number of individuals
2,983
2,565
2,128
2,005
F test H0: all the coef. are jointly zero18.43***
11.95***
12.08***
10.93***
F test H0: all the coef. on the exclusion restriction 19.13***15.94***
19.34***
17.25***
are jointly zero1) *, ** and *** indicate statistical significance at the 10%, 5% and 1% levels, respectively.
2) Figures reported in square brackets are robust standard errors adjusted for clustering.
3) The coefficients of the other explanatory variables are not reported.